AI Quality Assurance Intern

Toronto, Canada (Hybrid)Hybridentry

OtherQuality Assurance

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Quick Summary

Key Responsibilities

You will help ensure our AI systems act as intended, follow correct business logic, and provide accurate and safe outputs to end users. As an AI Quality Assurance Intern,

Requirements Summary

This is an excellent fit for students interested in: AI/ML, NLP, or chatbots Product quality and user experience Linguistics, conversation analysis,

Technical Tools

OtherQuality Assurance

Cresta is on a mission to turn every customer conversation into a competitive advantage by unlocking the true potential of the contact center. Our platform combines the best of AI and human intelligence to help contact centers discover customer insights and behavioral best practices, automate conversations and inefficient processes, and empower every team member to work smarter and faster. Born from the prestigious Stanford AI lab, Cresta's co-founder and chairman is Sebastian Thrun, the genius behind Google X, Waymo, Udacity, and more. Our leadership also includes CEO, Ping Wu, the co-founder of Google Contact Center AI and Vertex AI platform, and co-founder, Tim Shi, an early member of Open AI.

Join us on this thrilling journey to revolutionize the workforce with AI. The future of work is here, and it's at Cresta.

About the role:

We are seeking curious, detail-oriented students to join our AI Delivery team as AI Quality Assurance Interns.
This is a hands-on role where you will evaluate the behavior, accuracy, and reliability of Cresta’s AI Agents and Agent Assist systems.
Unlike traditional QA, this work sits at the intersection of:

AI behavior analysis
Intent recognition evaluation
Conversation quality assessment
Model output auditing
Light prompt testing and refinement

Responsibilities

~1 min read

You will help ensure our AI systems act as intended, follow correct business logic, and provide accurate and safe outputs to end users.

As an AI Quality Assurance Intern, you will:

→Execute structured test plans for AI Agent implementations
→Validate that intents, entities, and workflows trigger correctly
→Identify behavioral inconsistencies, misclassifications, or hallucinations
→Review and validate AI-generated suggestions, summaries, and classifications
→Provide actionable feedback to improve intent models and redaction/PII handling
→Run bias, redaction, and transcription accuracy checks
→Spot patterns or emerging issues across test sets
→Contribute ideas for improving internal QA processes and tools